ALEV MUTLU, Furkan GOZ, Kubra KOKSAL, ARZU ERENER

Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study, we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set showed that the proposed method achieves 83.07% classification accuracy and is superior over most commonly used techniques for landslide susceptibility assessment such as logistic regression, support vector machines and artificial neural network.

PDF

___

A. Morales-Esteban, F. Martínez-Álvarez, A. Troncoso, J. Justo, C. Rubio-Escudero, “Pattern recognition to forecast seismic time series”, Expert Systems with Applications, vol. 37, no. 12, pp. 8333- 8342, 2010.

K. Asim, F. Martínez-Álvarez, A. Basit, T. Iqbal, “Earthquake magnitude prediction in Hindukush region using machine learning techniques”, Natural Hazards, vol. 85, no. 1, pp. 471-486, 2016.

H. Cloke, F. Pappenberger, “Ensemble flood forecasting: A review”, Journal of Hydrology, vol. 375, no. 3-4, pp. 613-626, 2009.

B. Bhattacharya, D. Solomatine, "Neural networks and M5 model trees in modelling water level–discharge relationship", Neurocomputing, vol. 63, pp. 381-396, 2005.

H. Langer, S. Falsaperla, A. Messina and S. Spampinato, "Perfomance of a new multistation alarm system for volcanic activity based on neural network techniques", in Second European Conference on Earthquake Engineering and Seismology, 2014.

J. Parra, O. Fuentes, E. Anthony, V. Kreinovich, “Use of Machine Learning to Analyze and – Hopefully – Predict Volcano Activity”, Acta Polytechnica Hungarica, vol. 14, no. 3, 2017.

UNISDR: Landslide Hazard and Risk Assessment", Unisdr.org. [Online]. Available: https://www.unisdr.org/files/52828_03landslidehazarda ndriskassessment.pdf. [Accessed: 31- Aug- 2018].

F. Dai, C. Lee, Y. Ngai, “Landslide risk assessment and management: an overview”, Engineering Geology, vol. 64, no. 1, pp. 65-87, 2002.

E. Topsakal, T. Topal, “Slope stability assessment of a re-activated landslide on the Artvin-Savsat junction of a provincial road in Meydancik, Turkey”, Arabian Journal of Geosciences, vol. 8, no. 3, pp. 1769-1786, 2014.

A. Erener, A. Mutlu, H. Sebnem Düzgün, “A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)”, Engineering Geology, vol. 203, pp. 45- 55, 2016.

C. Ozgen, “An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road”, PhD thesis, Middle East Technical University (2012).

P. Temel, “Evaluation of potential run-of river hydropower plant using multicriteria decision making in terms of environmental and social aspect”, PhD thesis, Middle East Technical University (2015).

J. Isotalo, “Basics of Statistics”, [Online] Available: https://www.mv.helsinki.fi/home/jmisotal/BoS.pdf [Accessed: 2019-01-17]

Q. Ding, W. Chen, H. Hong, “Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping”, Geocarto International, pp. 1-21, 2016.

C. Xu, X. Xu, F. Dai, J. Xiao, X. Tan, R. Yuan, “Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region", Journal of Earth Science, vol. 23, no. 1, pp. 97-120, 2012.

B. Pradhan, S. Lee, “Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling”, Environmental Modelling & Software, vol. 25, no. 6, pp. 747-759, 2010.

L. Wang, M. Guo, K. Sawada, J. Lin, J. Zhang, “Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models”, CATENA, vol. 135, pp. 271- 282, 2015.

B. Pradhan, “A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS”, Computers & Geosciences, vol. 51, pp. 350-365, 2013.

B. Feizizadeh, M. Roodposhti, T. Blaschke , J. Aryal, “Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping”, Arabian Journal of Geosciences, vol. 10, no. 5, 2017.

D. Kumar, M. Thakur, C. Dubey and D. Shukla, “Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India”, Geomorphology, vol. 295, pp. 115-125, 2017.

H. Saito, D. Nakayama, H. Matsuyama, “Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan”, Geomorphology, vol. 109, no. 3-4, pp. 108-121, 2009.

A. Shirzadi, D. T. Bui, B. T. Pham, K. Solaimani, K. Chapi, A. Kavian, H. Shahabi, I. Revhaug, “Shallow landslide susceptibility assessment using a novel hybrid intelligence approach”, Environmental Earth Sciences, vol. 76, no. 2, 2017.

H. R. Pourghasemi, K. Norman, “Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran”, Environmental Earth Sciences, vol. 75, no. 3, 2016.

I. N. Aghdam, P. Biswajeet, M. Panahi, “Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran”, Environmental Earth Sciences, vol. 76 no.6, 2017.

K. Kodama, Y. Iijima, X. Guo and Y. Ishikawa, “Skyline queries based on user locations and preferences for making location-based recommendations”, International Workshop on Location Based Social Networks, 2009

J. Yang et al, “Finding superior skyline points for multidimensional recommendation applications”, World Wide Web, vol. 15, no. 1, pp. 33-60, 2012

G. Stoupas et al, “Rainbow ranking: an adaptable, multidimensional ranking method for publication sets”, Scientometrics, vol. 118, no. 1, pp. 147-160, 2018

A. Sidiropoulos, “Gazing at the skyline for star scientists”, Journal of Informatics, vol. 10, no. 3, 2016.

E. Topsakal, "An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road", PhD thesis, Middle East Technical University, (2012).

Weka 3: Data Mining Software in Java, [Online] Available: https://www.cs.waikato.ac.nz/ml/weka/ [Accessed: 2018-04-10].